{"id":"W4415670154","doi":"10.1061/jtepbs.teeng-9018","title":"Assessment of the Long-Term Impacts of Highway–Railway Grade Crossing Countermeasures: A Bayesian Vector Autoregression Modeling Approach","year":2025,"lang":"en","type":"article","venue":"Journal of Transportation Engineering Part A Systems","topic":"Traffic and Road Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Support vector machine; Collision; Bayesian vector autoregression; Bayesian probability; Homogeneous; Set (abstract data type); Decision tree; Training set","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004445659,0.000200862,0.0004912792,0.0002123004,0.00006632487,0.0000400071,0.0002305091,0.0001213079,0.000001547001],"category_scores_gemma":[0.00001189627,0.0001441865,0.00024644,0.000312403,0.00003169393,0.0001829867,0.000003260202,0.0003015082,5.687261e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001317158,"about_ca_system_score_gemma":0.0001663322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000116135,"about_ca_topic_score_gemma":0.000007020798,"domain_scores_codex":[0.9980736,0.00003238621,0.00112485,0.0001055788,0.0004667059,0.0001968257],"domain_scores_gemma":[0.9991785,0.00004371172,0.0003346419,0.0001992985,0.000170476,0.00007335345],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001247678,0.00005063301,0.01967444,0.002075514,0.0002017093,0.000003864359,0.0007082494,0.9663641,0.01056921,0.0002509427,0.00004363934,0.00004514845],"study_design_scores_gemma":[0.0005756744,0.00002375008,0.2812702,0.00339246,0.0001123958,0.00001231475,0.00008746752,0.7133344,0.00102021,8.584697e-7,0.00005502833,0.000115157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5571839,0.001098294,0.4399372,0.00001041445,0.001505262,0.0001611332,0.00002312647,0.00004863113,0.00003208456],"genre_scores_gemma":[0.9987937,0.00004685696,0.001001394,0.0000015447,0.00009951417,0.000006514298,0.000008308624,0.00002986938,0.00001236482],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4416097,"threshold_uncertainty_score":0.5879753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01227714575521749,"score_gpt":0.245857142646227,"score_spread":0.2335799968910095,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}